Microservices

Building Microservices at Scale: Lessons from Production After working with microservices at Agoda and CP Axtra, handling millions of transactions daily, I’ve learned valuable lessons about what works and what doesn’t when scaling distributed systems. The Challenge When you’re processing millions of payment transactions or managing inventory across thousands of retail locations, every millisecond counts. Here are the key challenges we faced: Service Communication Overhead: Network latency between services Data Consistency: Maintaining consistency across distributed databases Service Discovery: Dynamic service registration and discovery Fault Tolerance: Graceful degradation when services fail Key Architectural Patterns 1. Event-Driven Architecture with Apache Kafka @Service public class PaymentEventPublisher { @Autowired private KafkaTemplate<String, PaymentEvent> kafkaTemplate; public void publishPaymentCompleted(Payment payment) { PaymentEvent event = PaymentEvent.builder() .paymentId(payment.getId()) .amount(payment.getAmount()) .status(PaymentStatus.COMPLETED) .timestamp(Instant.now()) .build(); kafkaTemplate.send("payment-events", event); } } 2. Circuit Breaker Pattern @Component public class PaymentServiceClient { @CircuitBreaker(name = "payment-service", fallbackMethod = "fallbackPayment") @Retry(name = "payment-service") @TimeLimiter(name = "payment-service") public CompletableFuture<PaymentResponse> processPayment(PaymentRequest request) { return paymentServiceClient.process(request); } public CompletableFuture<PaymentResponse> fallbackPayment(Exception ex) { return CompletableFuture.completedFuture( PaymentResponse.builder() .status(PaymentStatus.PENDING) .message("Payment queued for retry") .build() ); } } Performance Optimizations Database Sharding Strategy We implemented a sharding strategy based on customer segments: ...